aiqbench analysis
This paper introduces Federated Agent Optimization (FAO), a framework designed to enable distributed LLM agents to collaboratively improve capabilities—such as memory and tool use—without sharing raw private data. By treating agent optimization as a multi-objective problem involving policy, rewards, and skills, it addresses the limitations of conventional federated learning in privacy-constrained environments. The work outlines critical challenges and future directions for building trustworthy systems where agents exchange abstracted experience rather than direct trajectories.
Federated Agent Optimization
arXiv:2610.01195v1 Announce Type: new Abstract: Large language model (LLM) agents increasingly operate in private environments and accumulate valuable experience from task execution, tool use, feedback, and local knowledge. Yet such experience is distributed across organizations and cannot be directly shared because of privacy and proprietary constraints. Conventional federated learning is insufficient for this setting, as agent capabilities extend beyond model parameters to memory, tools, rewards, skills, and structured knowledge. In this paper, we formulate \textbf{Federated Agent Optimization (FAO)}, which studies how distributed agents can collaboratively improve through controlled information exchange while keeping raw data, complete trajectories, and private knowledge local. We define FAO as a multi-objective problem balancing agent utility, privacy leakage, and communication cost, and organize its optimization space across policy, memory, tool use, reward, and structured knowledge and skills. We further characterize how private experience can be abstracted, protected, aggregated, and adapted into transferable capabilities, providing a unified view of how agents can benefit from one another without direct experience sharing. Finally, we identify the key challenges of FAO and outline several promising directions for future research toward trustworthy federated agent systems.
Abstract:Large language model (LLM) agents increasingly operate in private environments and accumulate valuable experience from task execution, tool use, feedback, and local knowledge. Yet such experience is distributed across organizations and cannot be directly shared because of privacy and proprietary constraints. Conventional federated learning is insufficient for this setting, as agent capabilities extend beyond model parameters to memory, tools, rewards, skills, and structured knowledge. In this paper, we formulate \textbf{Federated Agent Optimization (FAO)}, which studies how distributed agents can collaboratively improve through controlled information exchange while keeping raw data, complete trajectories, and private knowledge local. We define FAO as a multi-objective problem balancing agent utility, privacy leakage, and communication cost, and organize its optimization space across policy, memory, tool use, reward, and structured knowledge and skills. We further characterize how private experience can be abstracted, protected, aggregated, and adapted into transferable capabilities, providing a unified view of how agents can benefit from one another without direct experience sharing. Finally, we identify the key challenges of FAO and outline several promising directions for future research toward trustworthy federated agent systems.
Artificial Intelligence (cs.AI)
arXiv:2610.01195 [cs.AI]
(or arXiv:2610.01195v1 [cs.AI] for this version)
https://doi.org/10.48550/arXiv.2610.01195 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website.
Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them.
Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs.
Full text under the publisher's open licence · 273 words
Related coverage
Netskope (NTSK) Q2 2027 Earnings Call Transcript
fool.com · Sep 10, 2026
Accelerating Federated Learning Research with AI Agents and NVIDIA FLARE Auto-FL
NVIDIA · Jun 9, 2026
Learning from Unreliable Trajectories: Adversarially-Robust Federated Q-Learning
arXiv · Oct 7, 2026
FedEHR-Agents: Federated Agentic Optimization for Automated EHR Modeling
arXiv · Sep 1, 2026